Add prompt caching support for AWS Bedrock

docs/models/bedrock.md

@@ -74,6 +74,79 @@ model = BedrockConverseModel(model_name='us.amazon.nova-pro-v1:0')
 agent = Agent(model=model, model_settings=bedrock_model_settings)
 ```
 
+## Prompt Caching
+
+Bedrock supports [prompt caching](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html) on Anthropic models so you can reuse expensive context across requests. Pydantic AI exposes the same three strategies as Anthropic:
+
+1. **Cache User Messages with [`CachePoint`][pydantic_ai.messages.CachePoint]**: Insert a `CachePoint` marker to cache everything before it in the current user message.
+2. **Cache System Instructions**: Enable [`BedrockModelSettings.bedrock_cache_instructions`][pydantic_ai.models.bedrock.BedrockModelSettings.bedrock_cache_instructions] to append a cache point after the system prompt.
+3. **Cache Tool Definitions**: Enable [`BedrockModelSettings.bedrock_cache_tool_definitions`][pydantic_ai.models.bedrock.BedrockModelSettings.bedrock_cache_tool_definitions] to cache your tool schemas.
+
+> [!NOTE]
+> AWS only serves cached content once a segment crosses the provider-specific minimum token thresholds (see the [Bedrock prompt caching docs](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html)). Short prompts or tool definitions below those limits will bypass the cache, so don't expect savings for tiny payloads.
+
+You can combine all of them:
+
+```python {test="skip"}
+from pydantic_ai import Agent, CachePoint, RunContext
+from pydantic_ai.models.bedrock import BedrockConverseModel, BedrockModelSettings
+
+model = BedrockConverseModel('us.anthropic.claude-sonnet-4-5-20250929-v1:0')
+agent = Agent(
+    model,
+    system_prompt='Detailed instructions...',
+    model_settings=BedrockModelSettings(
+        bedrock_cache_instructions=True,
+        bedrock_cache_tool_definitions=True,
+    ),
+)
+
+
+@agent.tool
+async def search_docs(ctx: RunContext, query: str) -> str:
+    return f'Results for {query}'
+
+
+async def main():
+    result1 = await agent.run(
+        [
+            'Long cached context...',
+            CachePoint(),
+            'First question',
+        ]
+    )
+    result2 = await agent.run(
+        [
+            'Long cached context...',
+            CachePoint(),
+            'Second question',
+        ]
+    )
+    print(result1.output, result1.usage())
+    print(result2.output, result2.usage())
+```
+
+Access cache usage statistics via [`RequestUsage`][pydantic_ai.usage.RequestUsage]:
+
+```python {test="skip"}
+from pydantic_ai import Agent, CachePoint
+
+agent = Agent('bedrock:us.anthropic.claude-sonnet-4-5-20250929-v1:0')
+
+
+async def main():
+    result = await agent.run(
+        [
+            'Reference material...',
+            CachePoint(),
+            'What changed since last time?',
+        ]
+    )
+    usage = result.usage()
+    print(f'Cache writes: {usage.cache_write_tokens}')
+    print(f'Cache reads: {usage.cache_read_tokens}')
+```
+
 ## `provider` argument
 
 You can provide a custom `BedrockProvider` via the `provider` argument. This is useful when you want to specify credentials directly or use a custom boto3 client:

pydantic_ai_slim/pydantic_ai/messages.py

@@ -622,6 +622,7 @@ class CachePoint:
     Supported by:
 
     - Anthropic
+    - Amazon Bedrock (Converse API)
     """
 
     kind: Literal['cache-point'] = 'cache-point'

pydantic_ai_slim/pydantic_ai/models/bedrock.py

@@ -208,6 +208,21 @@ class BedrockModelSettings(ModelSettings, total=False):
     See more about it on <https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html>.
     """
 
+    bedrock_cache_tool_definitions: bool
+    """Whether to add a cache point after the last tool definition.
+
+    When enabled, the last tool in the `tools` array will include a `cachePoint`, allowing Bedrock to cache tool
+    definitions and reduce costs for compatible models.
+    See https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html for more information.
+    """
+
+    bedrock_cache_instructions: bool
+    """Whether to add a cache point after the system prompt blocks.
+
+    When enabled, an extra `cachePoint` is appended to the system prompt so Bedrock can cache system instructions.
+    See https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html for more information.
+    """
+
 
 @dataclass(init=False)
 class BedrockConverseModel(Model):
@@ -299,7 +314,8 @@ async def count_tokens(
         Check the actual supported models on <https://docs.aws.amazon.com/bedrock/latest/userguide/count-tokens.html>
         """
         model_settings, model_request_parameters = self.prepare_request(model_settings, model_request_parameters)
-        system_prompt, bedrock_messages = await self._map_messages(messages, model_request_parameters)
+        settings = cast(BedrockModelSettings, model_settings or {})
+        system_prompt, bedrock_messages = await self._map_messages(messages, model_request_parameters, settings)
         params: CountTokensRequestTypeDef = {
             'modelId': self._remove_inference_geo_prefix(self.model_name),
             'input': {
@@ -374,6 +390,8 @@ async def _process_response(self, response: ConverseResponseTypeDef) -> ModelRes
         u = usage.RequestUsage(
             input_tokens=response['usage']['inputTokens'],
             output_tokens=response['usage']['outputTokens'],
+            cache_read_tokens=response['usage'].get('cacheReadInputTokens', 0),
+            cache_write_tokens=response['usage'].get('cacheWriteInputTokens', 0),
         )
         response_id = response.get('ResponseMetadata', {}).get('RequestId', None)
         raw_finish_reason = response['stopReason']
@@ -417,8 +435,9 @@ async def _messages_create(
         model_settings: BedrockModelSettings | None,
         model_request_parameters: ModelRequestParameters,
     ) -> ConverseResponseTypeDef | ConverseStreamResponseTypeDef:
-        system_prompt, bedrock_messages = await self._map_messages(messages, model_request_parameters)
-        inference_config = self._map_inference_config(model_settings)
+        settings = model_settings or BedrockModelSettings()
+        system_prompt, bedrock_messages = await self._map_messages(messages, model_request_parameters, settings)
+        inference_config = self._map_inference_config(settings)
 
         params: ConverseRequestTypeDef = {
             'modelId': self.model_name,
@@ -427,7 +446,7 @@ async def _messages_create(
             'inferenceConfig': inference_config,
         }
 
-        tool_config = self._map_tool_config(model_request_parameters)
+        tool_config = self._map_tool_config(model_request_parameters, settings)
         if tool_config:
             params['toolConfig'] = tool_config
 
@@ -481,11 +500,18 @@ def _map_inference_config(
 
         return inference_config
 
-    def _map_tool_config(self, model_request_parameters: ModelRequestParameters) -> ToolConfigurationTypeDef | None:
+    def _map_tool_config(
+        self,
+        model_request_parameters: ModelRequestParameters,
+        model_settings: BedrockModelSettings | None,
+    ) -> ToolConfigurationTypeDef | None:
         tools = self._get_tools(model_request_parameters)
         if not tools:
             return None
 
+        if model_settings and model_settings.get('bedrock_cache_tool_definitions'):
+            tools.append({'cachePoint': {'type': 'default'}})
+
         tool_choice: ToolChoiceTypeDef
         if not model_request_parameters.allow_text_output:
             tool_choice = {'any': {}}
@@ -499,12 +525,16 @@ def _map_tool_config(self, model_request_parameters: ModelRequestParameters) ->
         return tool_config
 
     async def _map_messages(  # noqa: C901
-        self, messages: list[ModelMessage], model_request_parameters: ModelRequestParameters
+        self,
+        messages: list[ModelMessage],
+        model_request_parameters: ModelRequestParameters,
+        model_settings: BedrockModelSettings | None,
     ) -> tuple[list[SystemContentBlockTypeDef], list[MessageUnionTypeDef]]:
         """Maps a `pydantic_ai.Message` to the Bedrock `MessageUnionTypeDef`.
 
         Groups consecutive ToolReturnPart objects into a single user message as required by Bedrock Claude/Nova models.
         """
+        settings = model_settings or BedrockModelSettings()
         profile = BedrockModelProfile.from_profile(self.profile)
         system_prompt: list[SystemContentBlockTypeDef] = []
         bedrock_messages: list[MessageUnionTypeDef] = []
@@ -613,10 +643,13 @@ async def _map_messages(  # noqa: C901
         if instructions := self._get_instructions(messages, model_request_parameters):
             system_prompt.insert(0, {'text': instructions})
 
+        if system_prompt and settings.get('bedrock_cache_instructions'):
+            system_prompt.append({'cachePoint': {'type': 'default'}})
+
         return system_prompt, processed_messages
 
     @staticmethod
-    async def _map_user_prompt(part: UserPromptPart, document_count: Iterator[int]) -> list[MessageUnionTypeDef]:
+    async def _map_user_prompt(part: UserPromptPart, document_count: Iterator[int]) -> list[MessageUnionTypeDef]:  # noqa: C901
         content: list[ContentBlockUnionTypeDef] = []
         if isinstance(part.content, str):
             content.append({'text': part.content})
@@ -674,8 +707,17 @@ async def _map_user_prompt(part: UserPromptPart, document_count: Iterator[int])
                 elif isinstance(item, AudioUrl):  # pragma: no cover
                     raise NotImplementedError('Audio is not supported yet.')
                 elif isinstance(item, CachePoint):
-                    # Bedrock support has not been implemented yet: https://github.com/pydantic/pydantic-ai/issues/3418
-                    pass
+                    if not content or 'cachePoint' in content[-1]:
+                        raise UserError(
+                            'CachePoint cannot be the first content in a user message - there must be previous content to cache when using Bedrock. '
+                            'To cache system instructions or tool definitions, use the `bedrock_cache_instructions` or `bedrock_cache_tool_definitions` settings instead.'
+                        )
+                    if 'text' not in content[-1]:
+                        # AWS currently rejects cache points that directly follow non-text content.
+                        # Insert an empty text block as a workaround (see https://github.com/pydantic/pydantic-ai/issues/3418
+                        # and https://github.com/pydantic/pydantic-ai/pull/2560#discussion_r2349209916).
+                        content.append({'text': '\n'})
+                    content.append({'cachePoint': {'type': 'default'}})
                 else:
                     assert_never(item)
         return [{'role': 'user', 'content': content}]
@@ -796,6 +838,8 @@ def _map_usage(self, metadata: ConverseStreamMetadataEventTypeDef) -> usage.Requ
         return usage.RequestUsage(
             input_tokens=metadata['usage']['inputTokens'],
             output_tokens=metadata['usage']['outputTokens'],
+            cache_read_tokens=metadata['usage'].get('cacheReadInputTokens', 0),
+            cache_write_tokens=metadata['usage'].get('cacheWriteInputTokens', 0),
         )
 
 

tests/models/cassettes/test_bedrock/test_bedrock_cache_point_adds_cache_control[claude-sonnet-4-5].yaml

@@ -0,0 +1,91 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": [{"text": "ONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\nONLY
+      SINGLE NUMBER IN RESPONSE\nONLY SINGLE NUMBER IN RESPONSE\n"}, {"cachePoint": {"type": "default"}}, {"text": "Response
+      only number What is 2 + 3"}]}], "system": [{"text": "YOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY
+      WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE
+      ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST
+      RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU
+      MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE
+      NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY
+      WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE
+      ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST
+      RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU
+      MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE
+      NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY
+      WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE
+      ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST
+      RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU
+      MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE
+      NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY
+      WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE
+      ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST RESPONSE ONLY WITH SINGLE NUMBER\nYOU MUST
+      RESPONSE ONLY WITH SINGLE NUMBER\n"}, {"cachePoint": {"type": "default"}}], "inferenceConfig": {}}'
+    headers:
+      amz-sdk-invocation-id:
+      - !!binary |
+        Y2RmYWJiOGYtYjM0MC00NzY4LTgwZTEtMDI5NzZiZDdiZjVm
+      amz-sdk-request:
+      - !!binary |
+        YXR0ZW1wdD0x
+      content-length:
+      - '5580'
+      content-type:
+      - !!binary |
+        YXBwbGljYXRpb24vanNvbg==
+    method: POST
+    uri: https://bedrock-runtime.us-east-1.amazonaws.com/model/us.anthropic.claude-sonnet-4-5-20250929-v1%3A0/converse
+  response:
+    headers:
+      connection:
+      - keep-alive
+      content-length:
+      - '321'
+      content-type:
+      - application/json
+    parsed_body:
+      metrics:
+        latencyMs: 2015
+      output:
+        message:
+          content:
+          - text: '5'
+          role: assistant
+      stopReason: end_turn
+      usage:
+        cacheReadInputTokenCount: 0
+        cacheReadInputTokens: 0
+        cacheWriteInputTokenCount: 1503
+        cacheWriteInputTokens: 1503
+        inputTokens: 14
+        outputTokens: 5
+        serverToolUsage: {}
+        totalTokens: 1522
+    status:
+      code: 200
+      message: OK
+version: 1